Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium
Abstract Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development s...
Saved in:
Published in | BMC medical informatics and decision making Vol. 23; no. 1; pp. 1 - 9 |
---|---|
Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
BioMed Central Ltd
13.09.2023
BioMed Central BMC |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Abstract
Background
Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.
Methods
This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.
Results
In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.
Conclusions
Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. |
---|---|
AbstractList | Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. Methods This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. Results In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. Conclusions Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. Keywords: Candidate predictor, Delirium, Expert judgment, Practice-based approach, Prediction model, Variable selection BackgroundPrognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.MethodsThis study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.ResultsIn the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.ConclusionsHeterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. Abstract Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. Methods This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. Results In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. Conclusions Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.BACKGROUNDPrognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.METHODSThis study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.RESULTSIn the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.CONCLUSIONSHeterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. Abstract Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. Methods This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. Results In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. Conclusions Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model. |
ArticleNumber | 181 |
Audience | Academic |
Author | Chen, Zhaoyi Ser, Sarah E Magoc, Tanja Prosperi, Mattia Bian, Jiang Solberg, Laurence M Lucero, Robert J Bjarnadottir, Ragnhildur I Snigurska, Urszula A |
Author_xml | – sequence: 1 fullname: Snigurska, Urszula A – sequence: 2 fullname: Ser, Sarah E – sequence: 3 fullname: Solberg, Laurence M – sequence: 4 fullname: Prosperi, Mattia – sequence: 5 fullname: Magoc, Tanja – sequence: 6 fullname: Chen, Zhaoyi – sequence: 7 fullname: Bian, Jiang – sequence: 8 fullname: Bjarnadottir, Ragnhildur I – sequence: 9 fullname: Lucero, Robert J |
BookMark | eNptkktr3DAUhU1JaR7tH-jK0E03TiVZ1mNVhtBHINBNuxbX0vWMBttyJXsg-_zwyjOh7ZQihMTVOd_linNdXIxhxKJ4S8ktpUp8SJRpSivC6ryZVBV9UVxRLlklNJcXf90vi-uU9oRQqermVXFZS0m41vyqeNpMU-8tzD6MZehKKKcIdvYWqxYSuhKmKQawu9KP5QGih7bHMmGP9mjpQjx60PlTYQgO-9LhAfswDTjOZZoX97iydyFNfoa-8qNbbGZnpY9-GV4XLzvoE755Pm-KH58_fb_7Wj18-3J_t3moLNdsrriQSqJm1LnWWrAt7YjijQWBRKEQTBDNeadaVdfQgrUMqNMNaio0EZzVN8X9iesC7M0U_QDx0QTw5lgIcWsg5tl7NE62vF69nbK8Jqiz3drMBspz6yazPp5Y09IO6GyeNEJ_Bj1_Gf3ObMPBUNIQohqeCe-fCTH8XDDNZvDJYt_DiGFJhinBlVaMrM3e_SPdhyWO-a9WVVM3UlH1R7WFPIEfu5Ab2xVqNlI0TGSYzqrb_6jycjh4mxPW-Vw_M7CTwcaQUsTu95CUmDWI5hREk4NojkE0tP4FGSbSUw |
Cites_doi | 10.1016/S0140-6736(13)60688-1 10.1136/fmch-2019-000262 10.1093/ageing/aft141 10.7326/0003-4819-119-6-199309150-00005 10.1111/j.1365-2648.2010.05525.x 10.1016/j.ejvs.2019.11.029 10.7326/M18-1377 10.1002/0471722146 10.1111/j.1532-5415.1988.tb04396.x 10.1186/s12877-020-01723-4 10.1056/NEJM199903043400901 10.1007/s11096-016-0312-7 10.1001/archinternmed.2007.4 10.1016/j.ijnurstu.2021.103932 10.1161/CIRCULATIONAHA.108.795260 10.1093/ageing/25.4.317 10.1136/bmjopen-2017-019223 10.1097/01.anes.0000278905.07899.df 10.1176/appi.books.9780890425787 10.5334/egems.237 10.1097/01.sla.0000077920.38307.5f 10.1177/0891988716666380 10.1111/j.1532-5415.1994.tb06551.x 10.7326/M14-0698 10.1371/journal.pmed.1001744 10.1097/MD.0000000000003072 10.13063/2327-9214.1079 10.1016/j.wneu.2020.03.160 10.1001/jama.1996.03530350034031 10.1001/archinte.167.15.1629 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2023 BioMed Central Ltd. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023. BioMed Central Ltd., part of Springer Nature. BioMed Central Ltd., part of Springer Nature 2023 |
Copyright_xml | – notice: COPYRIGHT 2023 BioMed Central Ltd. – notice: 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023. BioMed Central Ltd., part of Springer Nature. – notice: BioMed Central Ltd., part of Springer Nature 2023 |
DBID | AAYXX CITATION 3V. 7QO 7SC 7X7 7XB 88C 88E 8AL 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M0T M1P M7P P5Z P62 P64 PIMPY PQEST PQQKQ PQUKI Q9U 7X8 5PM DOA |
DOI | 10.1186/s12911-023-02278-1 |
DatabaseName | CrossRef ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central Advanced Technologies & Aerospace Database (1962 - current) ProQuest Central Essentials Biological Science Collection AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Health Management Database (Proquest) Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Biological Science Collection Advanced Technologies Database with Aerospace ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Biological Science Collection ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Health Management ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) Engineering Research Database ProQuest One Academic ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Nursing |
EISSN | 1472-6947 |
EndPage | 9 |
ExternalDocumentID | oai_doaj_org_article_d7b4395e9f8c430e9423ccf8ba14cac5 A765269829 10_1186_s12911_023_02278_1 |
GeographicLocations | United States |
GeographicLocations_xml | – name: United States |
GrantInformation_xml | – fundername: ; grantid: R33AG062884; R33AG062884 |
GroupedDBID | --- -A0 0R~ 23N 2WC 3V. 53G 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AAWTL AAYXX ABDBF ABUWG ACGFO ACGFS ACIWK ACPRK ACRMQ ADBBV ADINQ ADUKV AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS AQUVI ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C24 C6C CCPQU CITATION CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IHR INH INR ITC K6V K7- KQ8 LK8 M0N M0T M1P M48 M7P M~E O5R O5S OK1 P2P P62 PGMZT PIMPY PQQKQ PROAC PSQYO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB ABVAZ AFGXO AFNRJ 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PQEST PQUKI Q9U 7X8 5PM |
ID | FETCH-LOGICAL-c492t-46787e921ddbccacb1f0845ca6e08e66260944f8b833abacc2a1d95e916906423 |
IEDL.DBID | RPM |
ISSN | 1472-6947 |
IngestDate | Fri Oct 04 13:06:32 EDT 2024 Tue Sep 17 21:29:33 EDT 2024 Fri Aug 30 19:49:12 EDT 2024 Wed Sep 11 02:52:00 EDT 2024 Fri Feb 23 00:22:41 EST 2024 Fri Feb 02 04:38:46 EST 2024 Thu Sep 12 16:50:06 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Language | English |
License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c492t-46787e921ddbccacb1f0845ca6e08e66260944f8b833abacc2a1d95e916906423 |
Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500854/ |
PMID | 37704994 |
PQID | 2865357818 |
PQPubID | 42572 |
PageCount | 9 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_d7b4395e9f8c430e9423ccf8ba14cac5 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10500854 proquest_miscellaneous_2864898205 proquest_journals_2865357818 gale_infotracmisc_A765269829 gale_infotracacademiconefile_A765269829 crossref_primary_10_1186_s12911_023_02278_1 |
PublicationCentury | 2000 |
PublicationDate | 2023-09-13 |
PublicationDateYYYYMMDD | 2023-09-13 |
PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-13 day: 13 |
PublicationDecade | 2020 |
PublicationPlace | London |
PublicationPlace_xml | – name: London |
PublicationTitle | BMC medical informatics and decision making |
PublicationYear | 2023 |
Publisher | BioMed Central Ltd BioMed Central BMC |
Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
References | MZ Aung Thein (2278_CR4) 2020; 20 H Lindroth (2278_CR21) 2018; 8 KGM Moons (2278_CR22) 2019; 170 2278_CR2 SK Inouye (2278_CR8) 1993; 119 MA Pisani (2278_CR16) 2007; 167 JP Roijers (2278_CR18) 2020; 59 SK Inouye (2278_CR5) 1999; 340 2278_CR31 H Böhner (2278_CR26) 2003; 238 JM Leung (2278_CR13) 2007; 107 SE Levkoff (2278_CR14) 1988; 36 L Zhan (2278_CR20) 2020; 139 HA de Wit (2278_CR7) 2016; 38 JL Rudolph (2278_CR19) 2009; 119 ST O’Keeffe (2278_CR15) 1996; 25 2278_CR25 SK Inouye (2278_CR9) 1996; 275 MT Ji (2278_CR10) 2018; 22 RN Jones (2278_CR11) 2016; 29 MY Kim (2278_CR12) 2016; 95 G Nakagami (2278_CR30) 2021; 119 KG Moons (2278_CR24) 2015; 162 2278_CR29 RI Bjarnadottir (2278_CR32) 2018; 6 SK Inouye (2278_CR3) 2014; 383 MZI Chowdhury (2278_CR23) 2020; 8 J Sun (2278_CR27) 2012; 2012 L Kelly (2278_CR28) 2011; 67 DL Leslie (2278_CR1) 2008; 168 P Pompei (2278_CR17) 1994; 42 MP Carrasco (2278_CR6) 2014; 43 |
References_xml | – volume: 383 start-page: 911 issue: 9920 year: 2014 ident: 2278_CR3 publication-title: Lancet doi: 10.1016/S0140-6736(13)60688-1 contributor: fullname: SK Inouye – volume: 8 start-page: e000262 issue: 1 year: 2020 ident: 2278_CR23 publication-title: Fam Med Community Health doi: 10.1136/fmch-2019-000262 contributor: fullname: MZI Chowdhury – volume: 43 start-page: 346 issue: 3 year: 2014 ident: 2278_CR6 publication-title: Age Ageing doi: 10.1093/ageing/aft141 contributor: fullname: MP Carrasco – volume: 119 start-page: 474 issue: 6 year: 1993 ident: 2278_CR8 publication-title: Ann Intern Med doi: 10.7326/0003-4819-119-6-199309150-00005 contributor: fullname: SK Inouye – volume: 22 start-page: 424 issue: 4 year: 2018 ident: 2278_CR10 publication-title: Anaesth Pain & Intensive care contributor: fullname: MT Ji – volume: 67 start-page: 652 issue: 3 year: 2011 ident: 2278_CR28 publication-title: J Adv Nurs doi: 10.1111/j.1365-2648.2010.05525.x contributor: fullname: L Kelly – volume: 59 start-page: 598 issue: 4 year: 2020 ident: 2278_CR18 publication-title: Eur J Vasc Endovasc Surg doi: 10.1016/j.ejvs.2019.11.029 contributor: fullname: JP Roijers – volume: 170 start-page: W1 issue: 1 year: 2019 ident: 2278_CR22 publication-title: Ann Intern Med doi: 10.7326/M18-1377 contributor: fullname: KGM Moons – ident: 2278_CR29 doi: 10.1002/0471722146 – volume: 36 start-page: 1099 issue: 12 year: 1988 ident: 2278_CR14 publication-title: J Am Geriatr Soc doi: 10.1111/j.1532-5415.1988.tb04396.x contributor: fullname: SE Levkoff – volume: 20 start-page: 325 issue: 1 year: 2020 ident: 2278_CR4 publication-title: BMC Geriatr doi: 10.1186/s12877-020-01723-4 contributor: fullname: MZ Aung Thein – volume: 340 start-page: 669 issue: 9 year: 1999 ident: 2278_CR5 publication-title: N Engl J Med doi: 10.1056/NEJM199903043400901 contributor: fullname: SK Inouye – volume: 38 start-page: 915 issue: 4 year: 2016 ident: 2278_CR7 publication-title: Int J Clin Pharm doi: 10.1007/s11096-016-0312-7 contributor: fullname: HA de Wit – volume: 168 start-page: 27 issue: 1 year: 2008 ident: 2278_CR1 publication-title: Arch Intern Med doi: 10.1001/archinternmed.2007.4 contributor: fullname: DL Leslie – volume: 119 start-page: 103932 year: 2021 ident: 2278_CR30 publication-title: Int J Nurs Stud doi: 10.1016/j.ijnurstu.2021.103932 contributor: fullname: G Nakagami – volume: 119 start-page: 229 issue: 2 year: 2009 ident: 2278_CR19 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.108.795260 contributor: fullname: JL Rudolph – volume: 25 start-page: 317 issue: 4 year: 1996 ident: 2278_CR15 publication-title: Age Ageing doi: 10.1093/ageing/25.4.317 contributor: fullname: ST O’Keeffe – volume: 2012 start-page: 901 year: 2012 ident: 2278_CR27 publication-title: AMIA Annu Symp Proc contributor: fullname: J Sun – volume: 8 start-page: e019223 issue: 4 year: 2018 ident: 2278_CR21 publication-title: BMJ Open doi: 10.1136/bmjopen-2017-019223 contributor: fullname: H Lindroth – volume: 107 start-page: 406 issue: 3 year: 2007 ident: 2278_CR13 publication-title: Anesthesiology doi: 10.1097/01.anes.0000278905.07899.df contributor: fullname: JM Leung – ident: 2278_CR2 doi: 10.1176/appi.books.9780890425787 – volume: 6 start-page: 21 issue: 1 year: 2018 ident: 2278_CR32 publication-title: EGEMS (Wash DC) doi: 10.5334/egems.237 contributor: fullname: RI Bjarnadottir – volume: 238 start-page: 149 issue: 1 year: 2003 ident: 2278_CR26 publication-title: Ann Surg doi: 10.1097/01.sla.0000077920.38307.5f contributor: fullname: H Böhner – volume: 29 start-page: 320 issue: 6 year: 2016 ident: 2278_CR11 publication-title: J Geriatr Psychiatry Neurol doi: 10.1177/0891988716666380 contributor: fullname: RN Jones – volume: 42 start-page: 809 issue: 8 year: 1994 ident: 2278_CR17 publication-title: J Am Geriatr Soc doi: 10.1111/j.1532-5415.1994.tb06551.x contributor: fullname: P Pompei – volume: 162 start-page: W1 issue: 1 year: 2015 ident: 2278_CR24 publication-title: Ann Intern Med doi: 10.7326/M14-0698 contributor: fullname: KG Moons – ident: 2278_CR25 doi: 10.1371/journal.pmed.1001744 – volume: 95 start-page: e3072 issue: 12 year: 2016 ident: 2278_CR12 publication-title: Med (Baltim) doi: 10.1097/MD.0000000000003072 contributor: fullname: MY Kim – ident: 2278_CR31 doi: 10.13063/2327-9214.1079 – volume: 139 start-page: e127 year: 2020 ident: 2278_CR20 publication-title: World Neurosurg doi: 10.1016/j.wneu.2020.03.160 contributor: fullname: L Zhan – volume: 275 start-page: 852 issue: 11 year: 1996 ident: 2278_CR9 publication-title: JAMA doi: 10.1001/jama.1996.03530350034031 contributor: fullname: SK Inouye – volume: 167 start-page: 1629 issue: 15 year: 2007 ident: 2278_CR16 publication-title: Arch Intern Med doi: 10.1001/archinte.167.15.1629 contributor: fullname: MA Pisani |
SSID | ssj0017835 |
Score | 2.3893259 |
SecondaryResourceType | review_article |
Snippet | Abstract
Background
Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify... Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable... Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and... BackgroundPrognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable... Abstract Background Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify... |
SourceID | doaj pubmedcentral proquest gale crossref |
SourceType | Open Website Open Access Repository Aggregation Database |
StartPage | 1 |
SubjectTerms | Adults Analysis Candidate predictor Care and treatment Delirium Electronic health records Electronic medical records Expert judgment Heterogeneity Hospital patients Hospitals Literature reviews Medical records Mental disorders Nursing Older people Patients Practice-based approach Prediction model Prediction models Prognosis Risk factors Structured data Validation studies Variable selection Variables |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA7iQbyIT6wvIggeJNhH2iTHVRQR9KTgLeRVXNCuuLv-An-4M2mrVg9evOZFmpl0ZjIz3xByVIjcFdIKJmxRM85tYIobwVJuhXVlZVIfA2Rvq6t7fv1QPnwr9YUxYS08cHtwp15YkJllULV0vEiDAvnvXC2tybgzrkUvzcremOr8B_ie0afIyOp0ClINnwJz9FnmaDcNxFBE6__9T_4ZJ_lN8FyukpVOY6SjdqdrZCE062TppvOJb5D30ZcPmk5qamif-cRQRHnaw4bTcUPfwDTGZCk6jfVvcAporXEOLhgbYnEc6r-iiWjEoMW1H7sqIwwseeAJD6Oexq_j-fMmub-8uDu_Yl1tBea4ymcM_o9SBJVn3lsgorNZnUpeOlOFVIYKzRzFORyzLApjjXO5yTySAd1qYLMUW2SxmTRhm1DJQ208DKqM4kI5w9FmBNMmiFC7UCfkpD9q_dJCaOhoeshKt4TRQBgdCaOzhJwhNT5HIvx1bACm0B1T6L-YIiHHSEuNlxQoB01trgFsGOGu9EhUWFld5iohe4ORcLncsLvnBt1d7qnGZF4ECcpkQg4_u3EmBqw1YTKPY7iEFVLYixxw0eDLhj3N-DECfIPOi6ow3_mPs9gly3lkfMWyYo8szl7nYR8UqZk9iHfmA2E4H00 priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfR3LitRAsFhXFC-iq2J0lRYED9JMHp1090lGcRkEPbkwt6ZfcQfcZJ2HX-CHW9WTzBgFr-l3qqq73gXwupKlr5STXLqq5UK4yLWwkufCSefrxuYhOch-aRaX4tOyXp7AYoyFIbfK8U5MF3XoPenIZxRBSZlZCjWzjrQAfjt7d_ODU_0osrMOxTRuwe2iRLYCMVsuD6JXQfqNMWRGNbMNvnKkGizJhlmSHDV5llL2_n_v6L_9Jv94iC4ewP2Bg2TzPcgfwknszuDu58FGfgZ3BgXAI_g1P1qnWd8yy8aYKE6PV2BjQnG26thPFJopjIptUmUcGoL8bBpDU6cPqWwOC0c_I5ay09LcV0P9EY4yPmJLwF7fV-vV7voxXF58_PphwYeqC9wLXW453pxKRl0WITgEr3dFmytRe9vEXMWGBCAtRKucqirrrPelLYKuoyaDG0oz1RM47fouPgWmRGxtwE6N1UJqbwVJkyj0RBlbH9sM3o4_3dzsk2uYJJSoxuxBZBBEJoHIFBm8J7gcelJi7PShX38zA52ZIB2yWLibVnmBC9Fy3uNubSHwMHUGbwiqhsiXkMgOUQi4YUqEZeayoZrrqtQZnE96Itn5afOIF2Yg-405ImkGrw7NNJJc2brY71IfoXCGHPeiJvg0Odm0pVtdpdTfyA0Tkyye_X_153CvTMiteVGdw-l2vYsvkHnaupeJLn4DMCwcuQ priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Nb9UwDI_GkBCXifEhOgYKEhIHFGhTt0kOCD2mTRPSOPGk3aIkTdmTtj54Hwju_OHYee0eZbtwTZw0je3Gvzq2GXtVKhlK7ZVQvmwFgI_CgFMiB698qGqXN-mC7Of6dAqfzqvzHTaUO-o3cHkrtKN6UtPF5duf3399QIV_nxRe1--WeGbRjz5JHklJqOgOuyuhBJL4M9h6FegvR4o2UlLUBtQQRHPrHKODKuXzv_nV_vcm5V9H08kDttfblHyyEYJ9thO7h-zeWe81f8R-T7Zeaj5vueNDbJSgQ6zhQ2JxPuv4DwTPFE7Fl6lCDg1BuzaNoQlTQyqfw5vtfSOestTS3Bd9HRKBWB-lpkGqy9litr56zKYnx1-OTkVffUEEMHIl8AuqVTSyaBqPbA6-aHMNVXB1zHWsCQgZgFZ7XZbOuxCkKxpTRUOON0Q15RO22827-JRxDbF1DRLVDnfdBAeEKhH8RBXbENuMvRm22n7bJNmwCZzo2m4YY5ExNjHGFhn7SNy4pqQE2alhvvhqe32zjfJoauFqWh0AH0SPCwFX6wrAl6ky9pp4aUmwkHPYtIlGwAVTQiw7UTXVXtfSZOxwRInqF8bdgzTYQXothftSGqFCZ-zldTeNpCttXZyvEw1onCHHteiRFI3ebNzTzS5SCnC0islYhoP_2rln7L5MEm5EUR6y3dViHZ-jTbXyL5Ki_AGLKR6_ priority: 102 providerName: Scholars Portal |
Title | Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium |
URI | https://www.proquest.com/docview/2865357818/abstract/ https://www.proquest.com/docview/2864898205/abstract/ https://pubmed.ncbi.nlm.nih.gov/PMC10500854 https://doaj.org/article/d7b4395e9f8c430e9423ccf8ba14cac5 |
Volume | 23 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3LatwwUCQphFxKn9RtuqhQ6KEou7ZkSzruhmxDYUMIDSy9CEmWG0PWG_bRL8iHZ0ZrJ3V760UHaSSPPSNrRvMi5DOXmefKSSYdr5gQLjAtrGQj4aTzeWFHZXSQvSjOr8X3eT7fI0UXCxOd9r2rT5rbxUlT30TfyruFH3Z-YsPL2SnIBCgqiOE-2Zecdzp6azvAu4wuPEYVwzWcaHgNmKG9MkOd6YgccilR1he90ygm7f_31_y3u-Qf58_0BXneCo50vEPwJdkLzStyOGtN46_J_fjJFE2XFbW0C4BieFKVtMseTuuG_gYNGWOm6DqWwcEpILzGObhg7Ig1cmj55FREYypaXPumLTbCQKEH1igB6rZe1dvFG3I9Pftxes7aEgvMC51tGPwmlQw6S8vSAS29S6uRErm3RRipUKC2o4WolFOcW2e9z2xa6jxotK6B6sLfkoNm2YR3hCoRKlsCUGG1kNpbgaojaDhBhsqHKiFfu09t7naZNEzUQFRhdjQyQCMTaWTShEyQGo-QmAU7dixXv0zLC6aUDuQpwKZSXsCD8HHeA7Y2FfAyeUK-IC0N7lWgHHTtQg4AYcx6ZcaywALrKtMJOe5Bwh7z_eGOG0y7x9cGY3oxV1CqEvLpcRhnot9aE5bbCCMUrDACXFSPi3pv1h8Bto95vjs2f___Uz-QoyxyvmYpPyYHm9U2fAQpauMGsHXmElo1_TYgzyZnF5dXg3gjAe1MKGivJj8HcWs9AFHaJYI |
link.rule.ids | 230,315,733,786,790,870,891,2115,2236,12083,12792,21416,24346,27957,27958,31754,31755,33408,33409,33779,33780,43345,43635,43840,53827,53829 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfR3ZbhMx0IJUHC8ICohAASMh8YCs7uFd208oRa0CtBFCrdQ3y9fSSLBbcvAFfDgzjjdhQeLVHh-7M2PPeC5CXpeicKW0gglbNoxzG5jiRrCMW2FdVZvMRwfZWT294B8vq8v04LZMbpX9mRgPat85fCM_xAhKzMySy3fXPxhWjULraiqhcZPs8RJUlRHZOzqeff6ytSPgu0YfKiPrwyXcbvgkWKDtskD9aXAdxaz9_57Nf_tL_nEBndwn95LkSCcbVD8gN0K7T26fJdv4PrmVFP-H5NdkZ5WmXUMN7WOhGF5anvaJxOm8pT9BWcbwKbqMFXFwCMixcQxOHRtiuRzqd_5FNGalxbmvUt0RBro9UIkHqG_zxXz9_RG5ODk-fz9lqdoCc1wVKwYnphRBFbn3FtDqbN5kklfO1CGToUbFR3HeSCvL0ljjXGFyr6qg0NAGWkz5mIzarg1PCJU8NMYDUG0UF8oZjlokKDtBhMaFZkze9j9dX2-SauiojMhab1CkAUU6okjnY3KEeNlCYkLs2NAtvurEX9oLC6IV7KaRjsNCuJxzsFuTc_iYakzeIFY1si3gEJo20QewYUyApSeixlrrslBjcjCABHZzw-6eLnRi96XeEeeYvNp240h0YWtDt44wXMIMGexFDuhp8GXDnnZ-FVN-gxSMwjF_-v_VX5I70_OzU336YfbpGblbREJXLC8PyGi1WIfnIECt7IvEJb8BEzcdUg |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagSKteeCPSFjASEgeU3Tyc2D4uhVV5tOqBSpU4WH6FRnSzq31w4MwPZ8ZJWlJuvdpjZyx_tmcyL0Le5DyzuTA85iavYsaMjyXTPE6Y4cYWpU5ccJA9KY_O2Ofz4rzzqlx3bpWNNfW4uZyPm_oi-FYu53bS-4lNTo8PQSZAUYFNlq6a3CX34NBmstfUOwsC_tHog2REOVnDu4Y_AzO0WmaoOe2SUc45Svxs8CaF1P3_X9A3nSb_eYVmD8j3nv_W-eTneLsxY_v7RmrH2y3wIbnfCad02tI8Ind885iMjjvz-xPyZ3pt7qaLimraB1nF-Bo62mcop3VDf4EWjnFZdB1K7eAQEJDDGJwwNIQ6PNRdOy7RkO4W577oCprEdeMAfg6oLutVvZ0_JWezj98Oj-KujENsmcw2MVzFgnuZpc4ZwIs1aZUIVlhd-kT4EjUqyVgljMhzbbS1mU6dLLxECx6oR_kzstMsGv-cUMF8pR0QlVoyLq1mqJ6CFuW5r6yvIvKu30i1bLN1qKDliFK1CFCAABUQoNKIvMe9vqLETNuhYbH6obrtUI4bkNmAm0pYBh_Cz1kL3OqUwWKKiLxFpCi8DwAX0NSGNQDDmFlLTXmJRdwBoBE5GFDCObbD7h5rqrtH1grjhjEfUSoi8vqqG0eib1zjF9tAwwTMkAAvYoDRwcqGPYC8kEu8R9re7Ye-IqPTDzP19dPJl32ym4UjJuM0PyA7m9XWvwChbWNehtP5FxJZQ90 |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Application+of+a+practice-based+approach+in+variable+selection+for+a+prediction+model+development+study+of+hospital-induced+delirium&rft.jtitle=BMC+medical+informatics+and+decision+making&rft.au=Snigurska%2C+Urszula+A.&rft.au=Ser%2C+Sarah+E.&rft.au=Solberg%2C+Laurence+M.&rft.au=Prosperi%2C+Mattia&rft.date=2023-09-13&rft.issn=1472-6947&rft.eissn=1472-6947&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1186%2Fs12911-023-02278-1&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s12911_023_02278_1 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1472-6947&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1472-6947&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1472-6947&client=summon |